def test_fit_cards():
with test_past_data_session_scope() as ts:
df_cards = fit_card_points(gameweek=1, season="1819", dbsession=ts)
assert isinstance(df_cards, pd.Series)
assert len(df_cards) > 0
assert all(df_cards <= 0)
assert all(df_cards >= -3)
def calc_all_predicted_points(
gw_range,
season,
include_bonus=True,
include_cards=True,
include_saves=True,
num_thread=4,
tag="",
dbsession=None,
):
"""
Do the full prediction for players.
"""
model_team = get_fitted_team_model(season,
gameweek=min(gw_range),
dbsession=dbsession)
model_file = pkg_resources.resource_filename(
"airsenal", "stan_model/player_forecasts.pkl")
print("Loading pre-compiled player model from {}".format(model_file))
with open(model_file, "rb") as f:
model_player = pickle.load(f)
df_player = get_all_fitted_player_models(model_player, season, gw_range[0])
if include_bonus:
df_bonus = fit_bonus_points(gameweek=gw_range[0], season=season)
else:
df_bonus = None
if include_saves:
df_saves = fit_save_points(gameweek=gw_range[0], season=season)
else:
df_saves = None
if include_cards:
df_cards = fit_card_points(gameweek=gw_range[0], season=season)
else:
df_cards = None
players = list_players(season=season,
gameweek=gw_range[0],
dbsession=dbsession)
if num_thread is not None and num_thread > 1:
queue = Queue()
procs = []
for _ in range(num_thread):
processor = Process(
target=allocate_predictions,
args=(
queue,
gw_range,
model_team,
df_player,
df_bonus,
df_saves,
df_cards,
season,
tag,
dbsession,
),
)
processor.daemon = True
processor.start()
procs.append(processor)
for p in players:
queue.put(p.player_id)
for _ in range(num_thread):
queue.put("DONE")
for _, p in enumerate(procs):
p.join()
else:
# single threaded
for player in players:
predictions = calc_predicted_points_for_player(
player,
model_team,
df_player,
df_bonus,
df_saves,
df_cards,
season,
gw_range=gw_range,
tag=tag,
dbsession=dbsession,
)
for p in predictions:
dbsession.add(p)
dbsession.commit()
print("Finished adding predictions to db")
def calc_all_predicted_points(
gw_range,
season,
include_bonus=True,
include_cards=True,
include_saves=True,
num_thread=4,
tag="",
player_model=ConjugatePlayerModel(),
dbsession=None,
):
"""
Do the full prediction for players.
"""
model_team = get_fitted_team_model(season,
gameweek=min(gw_range),
dbsession=dbsession)
print("Calculating fixture score probabilities...")
fixtures = get_fixtures_for_gameweek(gw_range,
season=season,
dbsession=dbsession)
fixture_goal_probs = get_goal_probabilities_for_fixtures(
fixtures, model_team, max_goals=MAX_GOALS)
df_player = get_all_fitted_player_data(season,
gw_range[0],
model=player_model,
dbsession=dbsession)
if include_bonus:
df_bonus = fit_bonus_points(gameweek=gw_range[0], season=season)
else:
df_bonus = None
if include_saves:
df_saves = fit_save_points(gameweek=gw_range[0], season=season)
else:
df_saves = None
if include_cards:
df_cards = fit_card_points(gameweek=gw_range[0], season=season)
else:
df_cards = None
players = list_players(season=season,
gameweek=gw_range[0],
dbsession=dbsession)
if num_thread is not None and num_thread > 1:
queue = Queue()
procs = []
for _ in range(num_thread):
processor = Process(
target=allocate_predictions,
args=(
queue,
gw_range,
fixture_goal_probs,
df_player,
df_bonus,
df_saves,
df_cards,
season,
tag,
dbsession,
),
)
processor.daemon = True
processor.start()
procs.append(processor)
for p in players:
queue.put(p.player_id)
for _ in range(num_thread):
queue.put("DONE")
for _, p in enumerate(procs):
p.join()
else:
# single threaded
for player in players:
predictions = calc_predicted_points_for_player(
player,
fixture_goal_probs,
df_player,
df_bonus,
df_saves,
df_cards,
season,
gw_range=gw_range,
tag=tag,
dbsession=dbsession,
)
for p in predictions:
dbsession.add(p)
dbsession.commit()
print("Finished adding predictions to db")
def calc_all_predicted_points(
gw_range,
season,
include_bonus=True,
include_cards=True,
include_saves=True,
num_thread=4,
tag="",
dbsession=None,
):
"""
Do the full prediction for players.
"""
model_team = get_fitted_team_model(
season, gameweek=min(gw_range), dbsession=dbsession
)
model_file = pkg_resources.resource_filename(
"airsenal", "stan_model/player_forecasts.pkl"
)
print("Loading pre-compiled player model from {}".format(model_file))
with open(model_file, "rb") as f:
model_player = pickle.load(f)
# model_player = get_player_model()
if include_bonus:
df_bonus = fit_bonus_points(gameweek=gw_range[0], season=season)
else:
df_bonus = None
if include_saves:
df_saves = fit_save_points(gameweek=gw_range[0], season=season)
else:
df_saves = None
if include_cards:
df_cards = fit_card_points(gameweek=gw_range[0], season=season)
else:
df_cards = None
all_predictions = {}
if num_thread:
queue = Queue()
procs = []
for i in range(num_thread):
processor = Process(
target=allocate_predictions,
args=(
queue,
gw_range,
model_team,
model_player,
df_bonus,
df_saves,
df_cards,
season,
tag,
dbsession,
),
)
processor.daemon = True
processor.start()
procs.append(processor)
for pos in ["GK", "DEF", "MID", "FWD"]:
queue.put(pos)
for i in range(num_thread):
queue.put("DONE")
for i, p in enumerate(procs):
p.join()
else:
# single threaded
for pos in ["GK", "DEF", "MID", "FWD"]:
predictions = calc_predicted_points_for_pos(
pos=pos,
team_model=model_team,
player_model=model_player,
df_bonus=df_bonus,
df_saves=df_saves,
df_cards=df_cards,
season=season,
gw_range=gw_range,
tag=tag,
dbsession=dbsession,
)
for k, v in predictions.items():
for playerprediction in v:
dbsession.add(playerprediction)
dbsession.commit()
print("Finished adding predictions to db for {}".format(pos))